ArticleCancer medicine2024
Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast-enhanced CT imaging: A multicenter study.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- CT-derived extracellular volume fraction and AI-assisted body composition for predicting neoadjuvant therapy response and prognosis in colorectal cancer.Abdominal radiology (New York) · 2026Article
- Epidemiological Characterizations and Diagnostic Strategies Variation in Parotid Gland Tumors (2010-2024): Observations Based on 2133 Cases.Cancer medicine · 2026Article
- Dual-energy computed tomography-derived extracellular volume fraction and spectral quantitative parameters for predicting early recurrence after gastrectomy: insights from a multicenter study.Abdominal radiology (New York) · 2026Article
- Incremental value of extracellular volume fraction based on CT for microsatellite status in colorectal cancer.Japanese journal of radiology · 2025Article
- Feasibility of extracellular volume fraction derived from single-phase spectral CT for differentiating between adrenal metastases and adenomas.Abdominal radiology (New York) · 2025Article
- Improved pre-surgical localization of epileptogenic tubers in pediatric tuberous sclerosis complex using mean apparent propagator MRI.Neurosurgical review · 2025Article
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10 authors.
Funding
Abstract
objectivesTo investigate the added value of extracellular volume fraction (ECV) and arterial enhancement fraction (AEF) derived from enhanced CT to conventional image and clinical features for differentiating between pleomorphic adenoma (PA) and atypical parotid adenocarcinoma (PCA) pre-operation.
methodsFrom January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT-derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong's test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application.
resultsAge and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145).
conclusionsECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.
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